Instructions to use Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF with PEFT:
```
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```
Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF:Q4_K_M

Ollama
How to use Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF with Ollama:
```
ollama run hf.co/Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF:Q4_K_M
```

Lemonade

How to use Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.CyberArchitect-Qwen-2.5-7B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

CyberArchitect-Qwen-2.5-7B-GGUF

Model Summary

CyberArchitect-Qwen-2.5-7B-GGUF is a specialized small language model fine-tuned for cybersecurity architecture review, infrastructure-as-code analysis, and threat modeling.
The model is trained to reason step-by-step before producing conclusions, reducing false positives commonly seen in general-purpose LLMs.

Author: Syamchand
Base Model: Qwen/Qwen2.5-7B-Instruct
Format: GGUF (Q4_K_M)
Fine-tuning Method: LoRA (Unsloth)
Intended Runtime: CPU / Mac / consumer hardware

Intended Use

This model is intended for defensive security (Blue Team) and educational or research purposes, including:

Kubernetes & cloud configuration reviews
Infrastructure-as-Code (IaC) security analysis
Architectural threat modeling
Secure coding logic validation
Identifying misconfigurations and insecure design patterns

Not Intended For

Automated vulnerability exploitation
Offensive security or malware development
Replacing professional security audits or production SAST tools

Model Behavior

The model is trained to follow a structured reasoning pattern, explicitly analyzing:

Data flow and trust boundaries
Authentication and authorization logic
Configuration context (cloud, IAM, K8s)
Security best practices vs actual implementation

It prioritizes context-aware reasoning over keyword matching.

Evaluation

Evaluation Setup

The model was evaluated against the base Qwen 2.5 7B Instruct using a curated internal dataset containing:

Secure code samples
Intentionally vulnerable code
Kubernetes YAML & cloud configuration files

Metrics (Qualitative)

Metric	Base Model	CyberArchitect
False Positive Rate	High	Low
Secure Pattern Recognition	Inconsistent	Strong
Reasoning Coherence	Generic	Structured & contextual
IaC / Cloud Understanding	Limited	Strong

Key Findings

Significantly reduced false positives on secure SQL and JWT handling
Improved reasoning on Kubernetes Secrets vs ConfigMaps
Better identification of architectural risks rather than surface-level issues

Limitations

Optimized primarily for infrastructure and architecture, not general conversation
May over-analyze authentication logic in small scripts
Reasoning quality depends on correct system prompt usage

Usage

System Prompt (Recommended)

You are a Cyber Architect. You must always start your response with a
'Thinking Process:' section where you analyze the request step-by-step,
followed by your final 'Analysis:'.

Example (llama-cpp-python)

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF",
    filename="Qwen2.5-7B-Instruct.Q4_K_M.gguf",
    n_ctx=4096,
    n_threads=4
)

Training Details

Fine-tuning: LoRA via Unsloth
Dataset: Custom Chain-of-Thought cybersecurity reasoning dataset
Hardware: NVIDIA T4 GPU
Quantization: GGUF via llama.cpp

Ethical Considerations

This model is designed strictly for defensive cybersecurity analysis.
It should not be used to generate exploits, bypass systems, or conduct malicious activities.

License

Apache 2.0

Downloads last month: 16

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for Syamchand/CyberArchitect-Qwen-2.5-7B-GGUF

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Adapter

(2839)

this model